The ship maneuverability based collision avoidance dynamic support system in close-quarters situation

With the continuous improvement of unmanned ship automation requirements, research into the automatic berthing of underactuated unmanned ships has important theoretical significance and practical value. In order to determine the trajectory of unmanned ships, the line of sight (LOS) algorithm was applied due to the characteristics of underactuated unmanned ships without side thrusters. In order to resist wind disturbance, the active disturbance rejection control (ADRC) method was applied to keep the ship moving on its intended trajectory. Then, to carry out the simulation analysis before the tank experiment, a remote-control simulation system based on a user datagram protocol (UDP) communication was built, and the ability of the ADRC controller to make the ship perform completely automatic berthing in both wind and no wind conditions was verified in simulations. Combined with the simulation results, a tank experiment was accomplished at the Japanese National Research Institute of Fishery Engineering. The experimental results also showed that the ADRC controller has good robustness, that the problems of insufficient autonomous route determination and the disturbance rejection ability in the process of the automatic berthing of underactuated unmanned ships are solved, and the safety of ship navigation is improved, which lays a theoretical and experimental foundation for the further development of unmanned ship control.

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“…The dimensions of the ship are shown in Table 2. [30,31]. The simulation results of the +35 deg turning test are shown in Figure 7.…”

Section: Automatic Berthing Simulationmentioning

confidence: 99%

“…Firstly, a ship maneuverability simulation was carried out to verify the accuracy of the ship mathematical model. The turning experiment and zig-zag experiment are the two most important experiments to test ship maneuverability [30,31]. The simulation results of the +35 deg turning test are shown in Figure 7.…”

Section: Automatic Berthing Simulationmentioning

confidence: 99%

Research into the Automatic Berthing of Underactuated Unmanned Ships under Wind Loads Based on Experiment and Numerical Analysis

Piao

Guo

Sun

2019

JMSE

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“…The Distance at Closest Point of Approach (DCPA) and Time to the Closest Point of Approach (TCPA) were two variables that indicate the probability of collision two vessels in the closest area. Figure 2: The DCPS and TCPA [8] The DCPA and TCPA were calculated from information of AIS from own ships and other ship (foreign ships). AIS on both vessels will provide static and dynamic in the certain period as it types.…”

Section: Calculate Of Dcpa and Tcpamentioning

confidence: 99%

Collision Avoidance Control – Accurate in Narrow Water Case Study: in West Line Tanjung Perak Surabaya, Indonesia

2019

Maritime Safety International Conference (MASTIC 2018)

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The study of an automatic control system to avoid collisions between ships has been done through the design of an autopilot system. The algorithms of control based on fuzzy and applicable of COLREGS rules. This study was conducted in numerical computer simulation in variety of control fuzzy methods. Fuzzy logic system works based on AIS data, and the output system is the speed and direction of the vessel that is capable of avoiding collisions with other vessels and foreign objects. A case study was conducted on the western line of Tanjung Perak Surabaya, Indonesia. The result of the avoidancecollision system shows that the accurate increase of the distance and the different heading between the vessels is precisely generated by the DCPA-TCPA inputs of fuzzy control.

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“…From the subsystems shown in Figure 1, this work is focused on the obstacle avoidance systems applied to USVs [8,10,[12][13][14][15][16][21][22][23][24][25][26][27][28][29][30][31]. Most of this work is focussed on the avoidance of dynamic obstacles in open sea situations [8,[12][13][14][15][21][22][23][24][25][26][27][28]. And, although some of them also consider a specific approach to static obstacles [15,22,23,25], their study and evaluation is secondary.…”

Section: Introductionmentioning

confidence: 99%

AutoTuning Environment for Static Obstacle Avoidance Methods Applied to USVs

Guardeño

López

Sánchez³

et al. 2020

JMSE

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This work is focused on reactive Static Obstacle Avoidance (SOA) methods used to increase the autonomy of Unmanned Surface Vehicles (USVs). Currently, there are multiple approaches to avoid obstacles, which can be applied to different types of USV. In order to assist in the choice of the SOA method for a particular vessel and to accelerate the pretuning process necessary for its implementation, this paper proposes a new AutoTuning Environment for Static Obstacle Avoidance (ATESOA) methods applied to USVs. In this environment, a new simplified modelling of a LIDAR (Laser Imaging Detection and Ranging) sensor is proposed based on numerical simulations. This sensor model provides a realistic environment for the tuning of SOA methods that, due to its low load computation, is used by evolutionary algorithms for the autotuning. In order to analyze the proposed ATESOA, three SOA methods were adapted and implemented to consider the measurements given by the LIDAR model. Furthermore, a mathematical model is proposed and evaluated for using as USV in the simulation enviroment. The results obtained in numerical simulations show how the new ATESOA is able to adjust the SOA methods in scenarios with different obstacle distributions.

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The ship maneuverability based collision avoidance dynamic support system in close-quarters situation

Cited by 96 publications

References 21 publications

Research into the Automatic Berthing of Underactuated Unmanned Ships under Wind Loads Based on Experiment and Numerical Analysis

Research into the Automatic Berthing of Underactuated Unmanned Ships under Wind Loads Based on Experiment and Numerical Analysis

Collision Avoidance Control – Accurate in Narrow Water Case Study: in West Line Tanjung Perak Surabaya, Indonesia

AutoTuning Environment for Static Obstacle Avoidance Methods Applied to USVs

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